Update EMT.py

EMT.py

@@ -1,165 +1,3 @@
-# import os
-# import datasets
-# import tarfile
-
-# _HOMEPAGE = "https://github.com/AV-Lab/emt-dataset"
-# _LICENSE = "CC-BY-SA 4.0"
-# _CITATION = """  
-# @article{EMTdataset2025,  
-#       title={EMT: A Visual Multi-Task Benchmark Dataset for Autonomous Driving in the Arab Gulf Region},  
-#       author={Nadya Abdel Madjid and Murad Mebrahtu and Abdelmoamen Nasser and Bilal Hassan and Naoufel Werghi and Jorge Dias and Majid Khonji},  
-#       year={2025},  
-#       eprint={2502.19260},  
-#       archivePrefix={arXiv},  
-#       primaryClass={cs.CV},  
-#       url={https://arxiv.org/abs/2502.19260}  
-# }  
-# """ 
-
-# _DESCRIPTION = """\
-# A multi-task dataset for detection, tracking, prediction, and intention prediction. 
-# This dataset includes 34,386 annotated frames collected over 57 minutes of driving, with annotations for detection and tracking.
-# """
-
-# # # Annotation repository
-# # _ANNOTATION_REPO = "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/annotations"
-
-# # Tar file URLs for images
-# _TRAIN_IMAGE_ARCHIVE_URL = "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/train_images.tar.gz"
-# _TEST_IMAGE_ARCHIVE_URL = "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/test_images.tar.gz"
-
-# # Tar file URLs for annotations
-# _TRAIN_ANNOTATION_ARCHIVE_URL = "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/train_annotation.tar.gz"
-# _TEST_ANNOTATION_ARCHIVE_URL = "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/test_annotation.tar.gz"
-
-
-# class EMT(datasets.GeneratorBasedBuilder):
-#     """EMT dataset."""
-
-#     BUILDER_CONFIGS = [
-#         datasets.BuilderConfig(
-#             name="full_size",
-#             description="All images are in their original size.",
-#             version=datasets.Version("1.0.0"),
-#         )
-#     ]
-
-#     def _info(self):
-#         return datasets.DatasetInfo(
-#             description=_DESCRIPTION,
-#             features=datasets.Features(
-#                 {
-#                     "image": datasets.Image(),
-#                     "objects": datasets.Sequence(
-#                         {
-#                             "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
-#                             "class_id": datasets.Value("int32"),
-#                             "track_id": datasets.Value("int32"),
-#                             "class_name": datasets.Value("string"),
-#                         }
-#                     ),
-#                 }
-#             ),
-#             supervised_keys=None,
-#             homepage=_HOMEPAGE,
-#             license=_LICENSE,
-#             citation=_CITATION,
-#         )
-
-#     def _split_generators(self, dl_manager):
-#         """Download train/test images and annotations."""
-#         image_urls = {
-#             "train": _TRAIN_IMAGE_ARCHIVE_URL,
-#             "test": _TEST_IMAGE_ARCHIVE_URL,
-#         }
-    
-#         # Download the tar file for annotations
-#         # annotation_urls = {
-#         #     "train": _TRAIN_ANNOTATION_ARCHIVE_URL,
-#         #     "test": _TEST_ANNOTATION_ARCHIVE_URL,
-#         # }
-#         annotation_urls = {
-#         "train": "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/train_annotation.tar.gz",
-#         "test": "https://huggingface.co/datasets/KuAvLab/EMT/resolve/main/test_annotation.tar.gz",
-#         }
-#         # Download image files
-#         images = {
-#             "train": dl_manager.iter_archive(image_urls["train"]),
-#             "test": dl_manager.iter_archive(image_urls["test"]),
-#         }
-    
-#         # Download annotation files and extract them
-#         annotations = {
-#             "train": dl_manager.download_and_extract(annotation_urls["train"]),
-#             "test": dl_manager.download_and_extract(annotation_urls["test"]),
-#         }
-    
-#         return [
-#             datasets.SplitGenerator(
-#                 name=datasets.Split.TRAIN,
-#                 gen_kwargs={
-#                     "images": images["train"],
-#                     "annotation_path": annotations["train"],
-#                 },
-#             ),
-#             datasets.SplitGenerator(
-#                 name=datasets.Split.TEST,
-#                 gen_kwargs={
-#                     "images": images["test"],
-#                     "annotation_path": annotations["test"],
-#                 },
-#             ),
-#         ]
-
-#     def _generate_examples(self, images, annotation_path):
-#         """Generate dataset examples by matching images to their corresponding annotations."""
-        
-#         annotations = {}
-        
-#         # Load all annotations into memory
-#         for ann_file in os.listdir(annotation_path):
-#             video_name = os.path.splitext(ann_file)[0]  # Get video folder name from the annotation file
-#             ann_path = os.path.join(annotation_path, ann_file)
-#             print("ann_path:,",ann_path,"\nannotation_path: ",annotation_path)
-            
-#             with open(ann_path, "r", encoding="utf-8") as f:
-#                 for line in f:
-#                     parts = line.strip().split()
-#                     if len(parts) < 8:
-#                         continue
-    
-#                     frame_id, track_id, class_name = parts[:3]
-#                     bbox = list(map(float, parts[4:8]))
-#                     class_id = _GT_OBJECT_CLASSES.get(class_name, -1)
-#                     img_name = f"{frame_id}.jpg"
-    
-#                     # Store annotation in a dictionary
-#                     key = f"{video_name}/{img_name}"
-#                     if key not in annotations:
-#                         annotations[key] = []
-    
-#                     annotations[key].append(
-#                         {
-#                             "bbox": bbox,
-#                             "class_id": class_id,
-#                             "track_id": int(track_id),
-#                             "class_name": class_name,
-#                         }
-#                     )
-        
-#         # Yield dataset entries
-#         idx = 0
-#         for file_path, file_obj in images:
-#             img_name = os.path.basename(file_path)
-#             video_name = os.path.basename(os.path.dirname(file_path))  # Match the video folder
-#             key = f"{video_name}/{img_name}"
-    
-#             if key in annotations:
-#                 yield idx, {
-#                     "image": {"path": file_path, "bytes": file_obj.read()},
-#                     "objects": annotations[key],
-#                 }
-#                 idx += 1
 
 import os
 import datasets
@@ -275,7 +113,7 @@ class EMT(datasets.GeneratorBasedBuilder):
         else:
             raise ValueError(f"Unknown annotation path: {annotation_path}")
     
-        ann_dir = os.path.join(annotation_path, annotation_split)
+        ann_dir = annotation_path
     
         print(f"Extracted annotations path: {annotation_path}")
         print(f"Looking for annotations in: {ann_dir}")